How to Create a Python ETL Task in Azure Functions with Blob Storage Integration?

Question

Hello,

I am working on an ETL pipeline using Azure Functions, and I need some guidance on the best approach and implementation details. Here’s what I am trying to achieve:

1. Trigger: I want to use an appropriate trigger to run my Azure Function whenever a new file is added to an Azure Blob Storage container.
2. Processing: The function should read the data from the source blob storage, process the data using Python, and then write the processed data to a destination blob storage.

Questions:

1. Trigger Selection: Which trigger is best suited for this scenario? I have heard about Blob triggers, but I am not sure if there are other triggers that might be more appropriate for this use case.
2. Implementation: Could someone provide a step-by-step guide or point me to relevant documentation on how to set this up?
3. Code Example: If possible, can you provide a simple Python code example demonstrating the read-process-write flow using Azure Functions and Blob Storage?

Thank you in advance for your help!

Answer 1

Hi @Aadhil Imam

The Blob Storage trigger would be the most appropriate for your scenario. This trigger starts a function whenever a new or updated blob is detected in the specified Blob Storage container.

For the implementation, you can follow these general steps:

1. Create an event-based Blob Storage triggered function in a new project.
2. Validate locally within Visual Studio Code using the Azurite emulator.
3. Create a blob storage container in a new storage account in Azure.
4. Create a function app in the Flex Consumption plan (preview).
5. Create an event subscription to the new blob container.
6. Deploy and validate your function code in Azure.

For a Python code example, you can refer to the following snippet:

import logging
import azure.functions as func
from azure.storage.blob import BlobServiceClient
def main(myblob: func.InputStream):
    logging.info(f"Python blob trigger function processed blob \n"
                 f"Name: {myblob.name}\n"
                 f"Blob Size: {myblob.length} bytes")
    
	# Create a blob service client
    blob_service_client = BlobServiceClient.from_connection_string("<your_connection_string>")
    
	# Get the container client
    container_client = blob_service_client.get_container_client("<your_container_name>")
    
	# Read the blob data
    blob_client = container_client.get_blob_client(myblob.name)
    data = blob_client.download_blob().readall()
    
	# Process the data
    processed_data = process_data(data)  # Replace with your data processing logic
    
	# Write the processed data to the destination blob
    dest_blob_client = container_client.get_blob_client("<destination_blob_name>")
    dest_blob_client.upload_blob(processed_data, overwrite=True)

Please replace <your_connection_string>, <your_container_name>, and <destination_blob_name> with your actual Blob Storage connection string, container name, and destination blob name, respectively. Also, replace process_data(data) with your actual data processing logic.

Additional reference: https://medium.com/@kvanshika94/connecting-to-azure-blob-storage-using-azure-functions-python-4fefa1adf66b

If this answer solves your problem, please mark it as accepted.